Effect of Rotational Speed, Dwell Time and Plunge Depth on Properties of Microstructure of Dissimilar Aluminum Sheet Metal by FSSW

FSSW process was developed by MAZDA Motor Corp and Kawasaki Heavy Industries to assemble the aluminum hood and rear doors of the RX-8 sport car. Since then FSSW has been used by Toyota Motor, General Motors, Ford Motor and many more etc. This technology getting interest from manufacturer of airplanes, appliances, and trains. The main goal of this study is to investigate the effect of process parameters on shear strength of FSSW joint and as well as analyze the mechanical and microstructure properties of the joint. In this present study the focus is on dissimilar alloys AA5754 and AA7075-T651 aluminum alloys. TRS, DT and PD imply an important role on heat production and material flow in FSSW, therefore it affect the development of microstructure and mechanical characteristics of the welded joint. Optical microscope, and scanning electron microscope (SEM) were conducted to characterize microstructure in welded joints. TSS and micro hardness test were used to evaluate the mechanical properties of joints. Many researchers have applied machine learning approaches like ANN to FSW but few authors have applied ANN to FSSW. In this research ANN has been applied to develop the met model of FSSW of dissimilar Al-Alloys of AA5754 and AA7075-T651. The obtained results reveal that the TSS of FSSW joint significantly affected by TRS, DT and with the tool PD. At constant TRS the PD and DT has significant effect on TSS. While on the other hand at constant DT the increment in TRS and PD has decrease the TSS. Keywords: Friction Stir Spot Welding, Full Factorial Design, Shear Tensile Strength, Micro hardness, Neural Network